Abstract: In response to the problems of cumulative errors and inaccurate correction results in the attitude calculation method of hydraulic supports based on inertial measurement units, a fully mechanized working face hydraulic support attitude monitoring method based on particle swarm optimization (PSO) - extreme learning machine (ELM) is proposed. Using the pitch angle of the hydraulic support top beam as the monitoring object, a tilt sensor and gyroscope are used to collect real-time information on the support attitude of the hydraulic support top beam. The collected data is preprocessed and input into the PSO-ELM error compensation model to obtain the predicted solution error. At the same time, the hydraulic support attitude is calculated through Kalman filtering fusion to obtain the calculated value. Then the method uses the error prediction value to compensate for the error in the calculated value, in order to obtain more accurate data on the top beam support attitude. This method only considers the relationship between acceleration and angular velocity data and solution errors, without relying on specific physical models. It can effectively reduce the cumulative error of attitude solution. The experimental results show that the average absolute error of the pitch angle of the top beam of the hydraulic support has been reduced from 1.420 8° before compensation to 0.058 0°. The error curve has good convergence, verifying that the proposed method can sustainably and stably monitor the support attitude of the hydraulic support.